Kevin Klaver, 5988144 MSc. Business Studies – Entrepreneurship and Innovation Track
University of Amsterdam, August 15th 2014 Supervisor: dr. T. Paffen Second supervisor: dr. ir. M.A.A.M. Leenders
Abstract
This study reviews biases which might hinder objective venture capital (VC) decision making. It is observed that the decision criteria VCs use have received much attention. However potential biases that might hinder the objective decision making have been largely neglected. This study proposes that concrete information in business plans feeds overconfidence of VCs in their own predictive ability regarding venture outcomes and that this overconfidence in turn negatively impacts their decision accuracy. However a conducted pre-survey to determine concreteness levels of business plans did not succeed in dividing these in two groups which were significantly different in terms of
concreteness. Which was necessary to set up an experiment in which the effect of data concreteness could be tested. Although the study could not tests its hypotheses,
implications for VC companies are discussed in anticipation on possible future successful replications of this study.
Table of Content
1 Introduction ... 4
2 Theoretical background ... 5
2.1 Biases in the venture capital investment decision process ... 8
2.2 Impact of data concreteness ... 15
3 Method ... 18
3.1 Participants and design ... 18
3.2 Independent Variable ... 19
3.3 Stimuli and procedure ... 21
4 Results ... 26
5 Discussion ... 27
5.1 Conclusions and Implications ... 27
1 Introduction
The average annualized internal rate of return between 1980 and 2013 (IRR) of
European venture capital funds is as little 1,68%, whereas venture funds in the top half received an IRR of 11.28% (European Venture Capital Association, 2014). This means that there is a substantial discrepancy in performance between the funds in the top half and in the bottom half.
One of the most important predictors of this value is the venture capitalists ability to select successful ventures. From a managerial point of view could it thus be argued that there is need to scientific research considering factors that could influence this ability. Because this could give VCs insights which might help them becoming better at selecting promising ventures.
These insights could, for example, help them taking measures to reduce the impact of biases which hinder VCs from making optimal decisions. On such bias is overconfidence. Zacharakis and Shepherd (2001) found that overconfidence has a negative impact on VCs decision accuracy when selecting high potential new ventures. According to Zacharakis and Shepherd (2001) does overconfidence in itself not
necessarily lead to a wrong decision, but this bias is likely to inhibit learning and improving the decision process.
This study zooms in at the overconfidence bias when VCs screen the business plans and make their first success predictions about the ventures. And looks at a potential predictor of this bias. Namely the concreteness of the data presented in business plans which could bias VCs. A study of Borgida and Nisbett (1977) namely showed that concrete data have a bigger impact on beliefs and decision making than abstract data. While at the same this type of data is much more likely to be challenged logically or disconfirmed empirically and thus weaker than abstract data. Therefore VC could be misled. Abstract data can thus lead to a false sense of confidence. This study investigates the effect of data concreteness on VCs confidence level and subsequently on their overconfidence level.
This study proceeds as follows. A method to test the corresponding hypotheses is presented in paragraph 3, after paragraph 2 which reviews the literature on biases in venture capital decision making. The results are presented in paragraph 4, and the conclusions, limitations and directions for future research are presented in paragraph 5.
2 Theoretical background
In the past three decades venture capital (VC) has evolved into a distinct industry within the financial services sectors of Western economies (Franke, Gruber, Harhoff, &
Henkel, 2006). VCs function as a major source of finance for new growth-oriented entrepreneurial ventures, particularly in the high-technology industries. Although growth-oriented ventures can be found in all industry sectors, there are some indications that the ventures with the highest growth potential are often characterized as
knowledge-based and technological driven - primarily based on intangible assets, operating in rapidly developing fields and with no documented history (Landström, 2007).
The absence of the documented history and the intangibility of the venture’s assets makes it difficult for these ventures to find capital for the growth of the business in the conventional capital market and also to gain access to the competence, experience and networks necessary for growth which most entrepreneurs lack (Landström, 2007). The venture capital market fulfils in this domain of growth-oriented entrepreneurial activities the need for adequate capital and management skills. Since this kind of ventures are almost excluded from the conventional capital markets, it can be argued that VC firms also serve as catalysts for innovation and renewal in the broader economy (Franke et al., 2006).
Franke et al. (2006) state that the success of VC firms themselves is largely determined by their ability to predict new firm performance during a multi-stage evaluation of investment proposals. Their special expertise in weeding out bad investment proposals is documented by research findings showing that VC-backed businesses achieve higher survival rates than non-VC-backed firms (Jain & Kini, 2000).
It is thus no coincidence that the evaluation process of VCs has received much attention by the research community in entrepreneurship and finance (e.g. Muzyka, Birley, & Leleux, 1996; Tyebjee & Bruno, 1984; Vinig & De Haan, 2001), as it is supposed to give valuable insights into the criteria that distinguish successful from unsuccessful firms (Franke et al., 2006).
These studies focussing on the criteria VCs employ to make their investment decisions have a relatively long tradition in entrepreneurship research, with the first studies ranging back to the 1970s (for an overview see, Hudson, 2005). These studies have produced a number of valuable insights into the VC decision process (Franke et al., 2006). The results are often interpreted as direct evidence on the long-term success factors of new firms (Franke et al., 2006), because the underlying assumption is that people who make money investing in new business by assessing the proposals should be experienced enough to distinguish between losers and winners (Riquelme & Rickards, 1992).
This led Franke et al. (2006) to conclude that the underlying assumption of this line of research is that venture capitalists are able to evaluate the success potential of start-ups objectively. Objectively in the sense that the VC estimation is unbiased, i.e., on average, the VC’s assessment predicts the actual success of the start-up correctly, leaving aside some random error. Franke et al. (2006) put forward two reasons to argument why one beliefs in the validity of this assumption.
First, Zacharakis and Meyer (1998) have argued that the evaluation process and the resulting selection decisions are crucial to the success of a VC firm and usually also to the individual VC’s personal income, by means of an interest performance fee (Cumming, Fleming, & Schwienbacher, 2007). Hence the VC firm itself and the individual evaluator have a strong incentive to avoid any form of bias. Second, evaluators should be able to avoid such biases, since they are typically highly experienced professional investors (Franke et al., 2006).
However, since decision makers are not perfectly rational, but bounded rational (Simon, 1955), research considering the potential biases would lead to a more realistic description of the decision making process of VCs.
Zacharakis and Shepherd (2001) observed that most of the research about VC decision making have listed the decision criteria VCs use when assessing new venture performance, while that research at the same time neglected potential biases that might hinder effective decision making.
Heuristics often underlie biases, heuristics, or ‘rules of thumb’, are sub-optimal decision strategies in that the decision maker does not fully utilize all available
information (Tversky & Khaneman, 1974, p. 1127; Simon & Hughton, 2002, p. 111). Thus heuristics, which are used consciously or unconsciously, filter out certain
information and allow the VCs to focus on other information (Zacharakis & Shepherd, 2007). However, what information VCs pay attention to, impacts their decision process and may result in decision biases (Zacharakis & Shepherd, 2007).
A study of Zacharakis and Meyer (2000) illustrates that VC decisions can be biased by the information VCs pay attention to. They namely found that the decision accuracy, in terms of predicting venture success, decreases when they have access to more decision factors, which is additional and thus other information. They suggested that decision makers utilize fewer cues than they think they do, ignoring the additional information or in some cases using it inappropriately.
In a more general sense the research conducted by Zacharakis and Meyer (2000) also indicates the need to consider various biases that can have an influence on the decision process when conducting research about VC. They showed that a model, which used the same criteria VCs say to make a decision, outperformed all actual VCs
participating in an experiment, in terms of predicting accuracy of venture outcomes. The VCs used the same information as was used in the model. These findings show that VCs can be biased in some way(s) because the model better predicted venture outcomes on the bases of exact the same information and criteria. This illustrates the necessity to research the determinants of the various biases which can affect decision accuracy.
These findings led Franke et al. (2006) to challenge the implicit assumption of earlier studies that evaluation by VCs can be treated as objective assessments of new venture quality disturbed only by a random error. They assert that research of these
biases is elementary for the understanding of the relationship between VCs and new ventures, and of success factors of new firms.
Up to present, research regarding VC decision making has focused on biases due to information processing or characteristics of the VC (Franke et al., 2006). VCs will want to avoid these biases in order to arrive at an undistorted evaluation. Next section reviews earlier research on biases which can play a role in the venture capital
investment decision process.
2.1 Biases in the venture capital investment decision process
Research with regard to characteristics of the VC has treated the similarity effect. The similarity effect, or the so-called, ‘similar-to-me’ hypothesis posit that individuals rate other people more positively the more similar they are to themselves (or the more similar the evaluator believes they are) (Franke et al., 2006).
Franke et al. found evidence that the similarity effect may play a role in VC decision making. They found that the more closely venture team members’ profiles resemble that of the VC with regard to education and the type of firm (start-up vs. large firm) where VC and members of the venture team have gathered prior professional experience, the better – on average - the venture team will be rated.
Franke et al. (2006) assert that they have found strong evidence supporting the notion of important similarity biases in the assessment process. They argue that from a rational point of view, VCs can assess a team sharing some characteristics with the VC more precisely than other VCs who do not have this particular experience. Then it can be expected that some of the assessments made by the well-informed VC would be more positive, and some more negative than those of other VC-raters. However they only found positive similarity effects.
Although Franke et al. (2006) did found that the similarity bias is at play in VC decision making, his findings need to be interpreted with caution because of the
particular set up of his experiment. The descriptions of the venture teams were namely much more descriptive in nature than the rest of the characteristics of the venture. The
following sentences were for example used to describe the non-team related aspects of a venture used in the experiment: project is based on patented technical product;
considerable cost savings for users; value proposition is clearly visible; potential users are small and medium-sized industrial firms; a working prototype exists. Which are more or less abstract descriptions. Whereas the team aspects were described in much more concrete terms, like age, level of education, field of education, relevant job experience et cetera. This difference in concreteness of the information can have implications for the actions that rest upon this information.
Abelson (1976) has namely argued that many inferences in everyday life proceed along the lines of pre-established cognitive ‘scripts’, which have implications for action. Nisbett, Borgida, Crandall & Reed (1976) have suggested that access to scripts is much more readily achieved by information that is concrete and vivid than by information that is pallid and remote. It is thus possible that the difference in
concreteness between the team and the non-team description may have caused the participants of the experiment of Franke et al. (2006) to assign more weight to the team then they do in reality. A more realistic experiment wherein the non-team related characteristics of the venture would also have been described in more concrete terms would have led to a more valid measurement of the similarity bias in VC decision making.
In a similar vein Murnieks, Haynie, Wiltbank and Haring (2011) also
investigated the similarity effect, but focussed their research on a similarity which is not directly observable, namely a similarity in decision-making processes. They found that VCs more favourably evaluate investment opportunities represented by individuals whose decision-making processes are similar to their own, when looking at two different entrepreneurial decision-making characterizations. Namely causation and effectuation. Causal decision-making processes ‘take a particular effect as given and focus on selecting between means to create that effect (Sarasvathy, 2001, p 245). Whereas, effectual decision-making processes ‘take a set of means as given and focus on selecting between possible effects that can be created with that set of means’ (Sarasvathy, 2001, p 245).
Murnieks et al. (2011) also found the decision-making-process similarity moderates the positive relationship between founder quality and the probability of investment such that as decision making process similarity increases, founder quality has a greater influence on the likelihood of investment. For two reasons, these two findings suggest that VCs are biased by their degree of similarity with entrepreneurs based on decision making processes. First neither causal nor effectual processes are represented in the literature as necessarily superior logics in an entrepreneurial context, nor as a preferred approach, instead, each strategy represents an alternative cognitive approach to be applied to entrepreneurial decision-making tasks (Murnieks et al., 2011). Second, it appears that both causation and effectuation are useful and prevalent in an entrepreneurial decision-making context (Sarasvathy, 2001).
Matusik, George and Heeley (2008) also look at a similarity effect, based on a similarity that is not directly observable, they looked at similarities based on values. They found that the more VCs value security the higher they evaluate founders with advanced degrees and the lower they will evaluate founders without advanced degrees. The argued that advanced degrees can viewed by VCs as indicative for a high valuation of security because these individuals who have attained this degrees have followed the social order to achieve success in ways that reinforce the stability of society and relationships. They, furthermore found that the more VCs value self-direction, the higher they will evaluate founders with start-up experience, other things being equal. They argued that what a founder does or does not learn in a particular situation is guided by his or her own route selection to procuring knowledge. And that the learning-by-doing activity, starting a company, requires independent thought and action, self-reliance, and self-sufficiency.
Research with regard to information processing has treated the phenomena, herding, gender bias, and overconfidence. Herding is the phenomenon where VCs invest in similar companies as do the leading VC firms, especially if the potential returns of that other VCs are likely to receive appear to be great (A. L. Zacharakis, 2010). Thus herding influences what kinds of ventures VCs pay attention to. Thereby is it likely that it also impacts how the information is interpreted, information is viewed
more favourably as late-coming VCs see the returns of earlier VCs (Zacharakis, 2010, Cumming, 2010). Sahlman and Stevenson (1986) have described the herding behaviour of VCs in the hard disk drive industry, which led to overinvestment and unsustainable valuations. Zacharakis and Shepherd (2007) argue that herding can be efficient because VCs can follow the lead and returns of other VCs as they seek attractive industries and investments, but the subsequent bubble has negative effects. It can namely lead to overcrowding in the market space with lots of ‘me-too’ competitors that damage the overall sector dynamics and increase the failure rate within that space (Zacharakis & Shepherd, 2007).
With regard to gender bias in VC decision making, Wuebker and Bigelow (2011) found that female founders may be disproportionately disadvantaged in their ability to attract capital, when controlling for firm, team, and CEO assessments, and the expected likelihood that the CEO would leave. This research indicates that VC decision making may be biased against women. However this research suffers from a sample bias, since the researchers used graduate student participants in a MBA course familiar with entrepreneurial finance as participants of their experiment. An replication of this study with professional VC investors would lead to more valid findings from a venture capital research perspective.
The third topic which received research attention with regard to information processing is overconfidence. Overconfidence has according to Griffin and Varey (1996, p 228) two types. First, optimistic overconfidence is the tendency to overestimate the likelihood that one’s favoured outcome will occur. Second, overestimation of one’s own knowledge is overconfidence in the validity of the judgment even when there is no personally favoured hypothesis or outcome.
Zacharakis and Sheperd (2001) assert that overconfidence influences the ability to accurately interpret information. However, this ability does influence the potential upside (or downside) of any investment. Mahajan (1992) found that if decision-makers are more confident, they will search less intensively for new information. Furthermore, overconfident decision makers are likely to commit resources without pausing to consider additional information (Mahajan, 1992). Last overconfidence can cause
decision makers to lose sight of the understanding of the limits of their own knowledge (Zacharakis & Shepherd, 2001). Taken together, VCs who are overconfident in their own decision process may rely on their existing knowledge instead of seeking
additional information. Hence, overconfidence may be problematic because it hinders the VC’s ability to accurately perceive potential opportunities and pitfalls. The more opportunities and pitfalls a VC can foresee in the early life of a venture, the greater the chance that the funded venture will generate high returns because the VC can carry out needed changes or reject the investment proposal altogether. Overconfidence has thus a negative effect in general on decision quality (Zacharakis & Shepherd, 2001), and may impact the VC fund performances.
Zacharakis and Sheperd (2001) did found that an increase in the level of overconfidence of VCs can weaken their prediction accuracy with regard to venture success. Their overconfidence level itself was dependent upon the type of information and the VC’s perception of success of a venture. VCs were more overconfident when predicting failure and success than when they predict moderate success. With regard to the type of information, Zacharakis and Sheperd (2001) showed that the presentation of task cues (task cues are not necessarily in the form with which the decision-maker is familiar or comfortable, these cues are statistically derived as those information factors that best distinguish between possible outcomes) led to more overconfidence than the presentation of cognitive cues (cognitive cues are decision criteria that expert VCs deem as best discriminating between success and failure), as input for the sample decision scenario’s which were used to measure accuracy.
To summarize, lots of research which has dealt with the decision process of VCs have neglected the biases which may prevent the VCs to assess new venture quality objectively disturbed only by random error. Bias due to information processing
(herding, gender bias and overconfidence) or characteristics of the VC (similarity bias) have received research attention up to now. This research has indicated that there are several biases at play and thereby increased our knowledge of the decision making process of VCs. But there is still room to broaden and deepen this knowledge. This study tries to deepen this knowledge with regard to the relationship between
overconfidence and the decision accuracy of VCs. Up to now there has been found evidence that overconfidence negatively impacts decision accuracy of VCs. And that VCs are more overconfident with decisions utilizing unfamiliar decision cues.
Furthermore, it has been found that VCs are more overconfident when predicting failure and success rather than moderate success.
Table 1
The Effect of the data Concreteness on individuals’ Decision Making.
Study Nature of research Impact of data concreteness on Decision making
Borgida and Nisbett (1977) Prospective psychology majors enrolled in introductory psychology took part in an
experiment (N=85). Some of these students were shown mean evaluation of upper level psychology courses (abstract data). Other students were exposed to face-to-face exposure to the comments of two or three students who had taken the courses (concrete data). After exposure to the course evaluation students were asked to fill out a “tentative schedule” of psychology courses for the rest of their college careers
The mean evaluations had little impact on course choices. Brief, face-to-face comments about the courses had a substantial impact on course choices.
Anderson (1983) Subjects (N=92) examined either two case histories (concrete data) or a statistical summary (abstract data) suggestive of either a positive or a negative relationship between fire fighter trainees’ level of preference for high risk and their
subsequent success as fire fighters. These data sets were equated for the initial strength of beliefs they induced. Subsequently subjects were thoroughly debriefed about the fictitious nature of their initial data. Directly and one week after debriefing subjects were subjects’ theory perseverance was measured.
Assessment of the subjects’ personal beliefs about the true relationship revealed (a) significant levels of theory perseverance both immediately and 1 week later; (b)
significantly more perseverance in the concrete data conditions, both immediately and 1 week later.
2.2 Impact of data concreteness
However there has not been attention for specific determinants of overconfidence with regard to the business plans where the VCs, at least in part, base their investment decisions upon. One such determinant which has not yet been researched in the context of venture capital, that might play a role in the realization of overconfidence may be the nature of the presented information in the business plans, in terms of concreteness. Are the foundations of the business plan based upon abstract data or concrete data? Several studies (Table 1) namely indicated that concrete data have a stronger effect on beliefs and decisions than abstract data (Anderson, 1983; Borgida & Nisbett, 1977). However it seems logical in the setting of venture selecting by VCs that it is more preferable to have more reliable data that are also more abstract – for instance statistical summaries of empirical studies. Because propositions based on abstract data are less likely to be challenged logically or disconfirmed empirically.
Given the findings of Anderson (1983), and, Borgida and Nisbett (1977), it might be expected that beliefs based on concrete data are held more strongly, or are more susceptible to perseverance biases. And thus also more susceptible to cause overconfidence, since one type of overconfidence is overconfidence in the validity of one’s own judgement even when there is no personally favoured hypothesis or outcome (Griffin & Varey, 1996).
Given the desirability of business plans to be based upon abstract data, in the light of its reliability and the potential of concrete data to cause VCs to become overconfident in their beliefs about the business plans, it is worthwhile to consider whether it is more likely that business plans are selected in the screening phase which are based upon concrete or abstract data. Then, can namely be considered whether it is worthwhile to investigate the impact of the concreteness of information presented in business plans on VCs’ decision making. The first step to discover that, is to look at the criteria VCs use in the screening period.
Petty and Gruber (2011, p. 181-182) found that, during the first two months of evaluating ventures by VCs, criteria related to product characteristics were among the
top three reasons for rejection. Earlier research has shown that these characteristics include sub criteria such as the innovativeness of the offering, some proprietary
protection of the product , and the level of need a potential customer has for the offering (Macmillan, Zemann and Subbanarasimha, 1987, p. 128). Taken together this indicates that VCs look for investment opportunities were they can fund a business introducing a pioneering product.
Simon and Houghton (2002) suggested that exactly entrepreneurs considering pioneering products are more inclined to use concrete data when taking decisions and drawing conclusions. They argue that pioneering products have a short history, so that there is probably little printed historical data, suggesting that the entrepreneurs may need to go directly to other individuals for information. Simon and Houghton (2002) further proposed that the pioneering entrepreneurs may utilize face-to-face meetings to gather some sense of the emerging market to describe the new market category.
Empirical research of Mohan-Neill (1995, p 18) partly corroborate this assertions, they found that older firms used formal methods of data collection with much more
frequency than did younger firms and that there was little significance difference in the use of informal methods. These findings imply that younger firms rely to a very great extend to personal information.
However, the findings of Mohan-Neill’s (1995) study do not specifically apply to pioneering young ventures. But following the line of thought of Simon and Houghton (2002) it can be argued that entrepreneurs of young firms considering introducing a pioneering product, are even more likely to utilize personal information, because of its relevance in new product market combinations.
Apart from that young ventures are less likely to have research expertise in-house to or the resources or pay a research supplier, so one can hypothesize that new ventures are less likely to use formal methods of data collection (Mohan-Neill, 1995), which would result in abstract data.
The above implicates that VCs will oftentimes consciously or unconsciously select business plans based upon concrete personal information. Since the use of this type of information might lead to a false sense of confidence, it is desirable to measure
the effect of the concreteness of the data on the confidence level of VCs, if any, and subsequently on their overconfidence level.
In order to determine the effect of the type of data on the (over)confidence level of VCs the following hypotheses based upon the above arguments have been proposed:
H1. Overconfidence adversely affects decision accuracy
H2a. VCs are more confident in their decisions when assessing business plans based
upon concrete information than when they are assessing business plans based upon abstract information.
H2b. VCs assessing business plans based upon concrete information are less
accurate in their decisions than when they are assessing business plans based upon abstract information.
H2c. VCs assessing business plans based on abstract information are more
overconfident.
In figure 1, depicts the proposed relations with regard to decision accuracy measured in terms of their predicting ability regarding venture outcomes. The next paragraph describes how the above hypotheses will be tested.
Overconfidence Nature of information (abstract/concrete) Accuracy H1 H2c
3 Method
3.1 Participants and design
The target population for this study are classical venture capital funds. As defined by Bygrave and Timmons (1992) as funds where capital is raised from patient investors, and managed by investors with entrepreneurial experience and industrial knowledge, who invest in early stage ventures and who actively operate in the companies in which they invest.
In the Netherlands are the most VCs unified in the organization called, Dutch association of participating enterprises (in Dutch: Nederlandse Vereniging van
Participatie Maatschappijen (NVP) (Durfkapitaal/Investeerders - verschaffers van private equity.2012). The members of this organisation made up the research sampling unit. NVP has also members who can be classified as merchant venture capital funds, these parties invest in more mature companies and normally have a short term investment horizon. These parties do not belong to the target population and will therefore be excluded from this research. The NVP classifies the classical venture capital funds as, funds which invest in companies in the seed, start-up or expansion phase. This
organisation has 59 associates who fall into this category. Of these 59 associates are 29 parties specialised in the IT sector. The investment managers of these 29 funds made up the target research population for testing the hypotheses.
An experiment would have been used to measure the impact of the type of information, in terms of concreteness, presented in business plans, on the decisions made by venture capitalists. An experiment has been chosen because, it tries to rules out the possible effects of alternative explanations to the planned intervention, by assigning respondents randomly to one of the two groups. VCs would have been randomly assigned to either group a, which would be exposed to business plans based upon concrete information or group b, which would be exposed to business plans based upon abstract information. In either case VCs would have been exposed to five actual VC funded business plans related to their own specialisation. These business plans would
have served served as so called decision cases. In fact this experiment has not been conducted. The next subparagraph makes clear why that has not been done.
3.2 Independent Variable
The level of concreteness (abstractness) of the business plans was tested with a pilot test. Therefore, 92 VCs, outside the sample of the study, independently of each other assessed these levels of ten executive summaries of business plans, from which the accompanying companies received VC funding. This was determined with a slightly adapted version of a scale develop by Yagade and Dozier (1990). Yagade and Dozier (1990) developed a scale to measure the concreteness level of issues covered in the media. The adapted scale had the following items:
1. Regarding this business plan, I can easily visualize the events happening. 2. I can easily see how this business plan is connected with things going on in the
world.
3. For me, this business plan is real.
4. When I think about this business plan, I get a clear picture of it in my head. 5. I feel I understand all the basics of this business plan.
6. For me, this business plan is easy to understand.
The respondents indicated how well the statement described how they felt about the issue on a 7-point Likert scale, ranging from “does not describe my feelings” to “describes by feelings well”. All six items were summed to form an additive index, called the ‘concreteness scale’. Business plans were considered concrete if the scored significantly (p < .05) higher than the average concreteness level of the ten business plans measured. And were considered abstract if they scored significantly (p < .05) lower than this level.
Mean scores on the concreteness scale were computed for each of the ten business plans, t-tests of the differences with the total mean on this scale were computed (table 1).
Unfortunately it turned out that only 3 business plans could be regarded as concrete and 2 as abstract using the above criteria. Therefore it was useless to the conduct the
experiment in order to test the hypotheses. Because of this, the next section describes how the experiment would have been conducted. The result section will discuss the reliability of the concreteness scale.
Tabel 1 - T-test of mean scores on concreteness scale with the total mean of 21,9273.
Business Plan Mean t
Ov-watch 24,5490 (n=51) 2,376*
Call Database 20,0882 (n=34) -1,227
JustBookit Solutions 16,9032 (n=31) -3,881*
SureWaves 25,3000 (n=30) 2,100*
VHT 22,5862 (n=29) ,430
Flat World Knowledge 25,2340 (n=47) 2,675*
Assay Depot 21,9706 (n=34) ,028
Voiceage 20,9032 (n=31) -,615
Appliansys 17,2381 (n=42 -3,976*
UbiquiSys 24,5000 (n=32) 1,455
* p is less than .05.
Electronic mail solicitations recruited participants among the VC community. The 2011 Global Venture Capital and Private Equity investor directory has been used for this. Solicitations have only been sent to VC managers/investors working for a VC company which invest in the IT sector in the seed, start-up or expansion phase. Which were 2579 people.
The 10 business plans have been divided into 3 pools of respectively five, three and two business plans in order to have these measured on the concreteness scale. 240
participants opened he hyperlink of the first set of business plans, 162 of them actually started the survey and 30 completed it. 251 opened the hyperlink of the second set, 156 started the survey and 30 have completed the survey. The hyperlink of the third set has 227 times been opened, and this survey has 152 times been started and has 30 times been completed. Which lead to a response rate of 3,6%.
In the second study, subjects would have been exposed to either concrete or abstract business plans. Following section describes how this experiment would have been executed.
3.3 Stimuli and procedure
The decision cases, which would have been categorized in the pilot study, were
obtained from VCs and individual ventures outside the sample of the study. All ventures involved have been funded and therefore have achieved some outcome. In every
decision case, the venture, the product and any associated individual were
depersonalized. This design ensured that participants would not have been biased by personal knowledge of the entrepreneur or the product in question.
After the VCs would have analysed each decision case, they would have been gauging how likely the potential venture was to succeed on a seven-point scale – 7 equated to very likely to succeed and 1 equated to very unlikely to succeed.
Subsequently they would have been asked to express their confidence in their success assessment using another seven-point scale. This scale was anchored by high
confidence (the venture exhibits all of the characteristics of a resounding
success/failure) and low confidence (no more confident the venture will succeed or fail than in predicting a head or a tails in a coin flip). In sum, the confidence level that the VC would have record should equate with the expected outcome of the venture (Zacharakis & Shepherd, 2001).
To assess the base level of overconfidence, the predictive ability (accuracy) for each VC would have been calculated as the proportion of correct VC responses (i.e., a comparison of the predicted performance with actual performance). Because the experiment only would include actual decision cases, the participating VC predictions could be compared with the actual outcomes of the ventures. Coding the VC response to success or failure by 5 to 7 (success probability is high), 4 (VC is unsure or doesn’t know), 1 to 3 (failure probability is high).
Ideal to measure the decision accuracy of VC’s, would be comparing their success predictions of ventures with their actual return on investment (ROI). When VC’s make investment decisions they namely try to predict the future cash flows of a venture and the derivable return on investment from these cash flows. However ROIs of ventures become normally known between 4-7 years after the actual investment, when a VC sells its equity.
This implicates that nearly only business plans of companies which are four years or older could have been included in the experiment. By doing this, the ability to predict venture success, of four to seven years old ventures, would be measured. Instead of their ability to predict venture success based upon current business plans, which is a aim of this study. Measuring decision accuracy on the basis of ROI is thus not possible in an experiment which is not longitudinal. Let alone, also measuring the impact of the nature of the presented information in terms of concreteness.
Therefore this study has used venture performance measures to determine the success of the ventures included in the experiment. The measures used in this study are based on the study of Wijbenga (2004). The measures used are organisational growth, goal effectiveness and labour productivity. The score out of these three measures would
have been compared with the success prediction of the VCs.
Organisational growth, consists of four items: Average sales increase, average sales growth rate, average number of employees increase and average employment
growth rate. Using three-year averages measure smoothens out yearly fluctuations in the data. These measures are computed by below formulas1:
Average sales increment= LNSales t=3 – LNSales t=0
3
Weighted average sales growth rate= LNSales t=3
LNSales t=0
Average employment increment = Nr of Employees t=3 – Nr of Employees t=0
3
Weighted average employment growth rate =
Nr of Employees t=3
Nr of Employees t=0
The second measure, goal effectiveness, was measured by subjective
performance scales, filled in by the entrepreneurs themselves. These kind of scales have
1 Reducing the large kurtosis of the distribution function (e.g., the flat distribution) of the sample
companies’ sales, a natural logistic (LN) transformation to make the performance indicator more normally distributed has been used, similar as Wijbenga (2004) did. In the formulas, t=0 comprises the year 2008 and t=3 comprises the year 2011.
-⅓ X 100% 1 -⅓ X 100% 1
high disclosure rate, strong internal consistency, and relatively strong inter-rater reliability (Chandler and Hanks, 1993). Sapienza (1992) found that VCs’ and
entrepreneur’s assessments on the items of the scale prove these measures to be highly reliable and valid. Labour productivity was gauged by the amount of sales per
employee. This measure is calculated as
Labour productivity = LNSales t=3
Nr of Empoyees t=3
The outcome of a venture was considered a success if it had record on at least two measures higher than the total mean of a specific measure2.
It can easily be argued that lots of thing can influence a venture’s ultimate success after funding. However, Roure and Keeley (1990, p. 216), assert that the initial investment decision is highly correlated with venture outcome. Put differently, ultimate venture success is predictable from characteristics of the new venture at prefunding, such as the entrepreneurial team, the nature of opportunity, and potential market growth. Which were described in the executive summaries which served as decision cases.
The level of confidence would have been calculated with the five investment decisions, each participant would have made. The level of a VC’s confidence (overconfidence, under-confidence, or perfect calibration) would subsequently have been determined by comparing the “confidence in the accuracy of a prediction” with the “accuracy of prediction” (Mahajan, 1992, p. 330). An individual would have been considered overconfident when their confidence would have been higher than his or her accuracy rate. And under-confidence would have been represented a confidence level below one’s accuracy (A. L. Zacharakis & Shepherd, 2001).
To analyse the data, an overconfidence index would have been calculated, which would have represented a ratio of confidence over accuracy. The confidence level would then have been measured as a mean of confidence ratings for each person,
converted to a percentage. An index below one would then have implied that a VC is under-confident, and an index above one would have represented overconfidence (Zacharakis & Shepherd, 2001).
Hypothesis 1 would have been tested using regression with the control variable, amount of years VC experience, because Shepherd, Zacharakis and Baron (2003) found that it was a significant predictor of the quality of VC investment decisions. Hypotheses 2a-c would have been tested with an independent t-test if the data would have met the normality assumption otherwise a Mann-Whitney Test would have been used.
4 Results
Unfortunately the only result to report on in this study is the reliability of the
concreteness scale which was being used. Since participants use the scale more than once in order to have the ten business plans being measured on the scale, the
Cronbach’s alphas could be computed ten times. The resulting values were high ranging from .893 to .96 with a mean value of .9295. The inter-item correlations (table 3), and the item to total correlations (table 2) measured by computing the Cronbach’s alpha for the Ov-Watch business plan are show below. All items within the scale had high item to total correlations (averaging .742, all exceeding .65), which indicate a high level of internal reliability.
Table 3 Inter-item correlations
5 Discussion
5.1 Conclusions and Implications
Does the concreteness of data presented in business plans feed the (over)confidence levels in the investment decisions of VCs? The answer to that questions remains after this study still answered. The pre-survey did not result in two pools of business plans which were different from each other in terms of concreteness, which could have been used in an experiment to test this hypothesis.
Although the study failed in answering this question. It is still one of managerial and scientific importance. Because many VC investment decisions are made on the basis of concrete data. Simply because there is a lack of more abstract data because the pioneering products and services where VCs seek to invest in have little printed
because of its statistical inferiority as it is likely formed by less observations than abstract data.
Research considering this concrete/abstract effect would thus lead to a more realistic description of the decision making process of VCs. This research can be used by VCs to identify ways to systematically decrease the potential overconfidence bias, which in turn leads to an improved decision accuracy (Zacharakis & Shepherd, 2001).
In the light of the substantial amount of 3,4 billion euros which was invested by European VCs in 2013 (2013 European Private Equity Activity) even a slightly increase in the prediction ability of venture success would result in considerable improved absolute allocation of their financial resources. Which would implicate that more viable ventures will be funded, this in itself will generate more long term positive effects, like increased employment and an increased amount of product innovations brought to the market, which all stimulate the level of prosperity.
On the other hand entrepreneurs could anticipate on the bias and taking advantage of it by presenting data that is more vivid and concrete rather than abstract and pallid. In this way their chances of getting funded (or being considered for funding) may increase due to this effect if a VC is positive regarding a venture. But it’s funding chances can at same time decrease if a VC is negative about a venture and develops a persevere negative opinion regarding the specific venture due to the concrete/abstract effect. Entrepreneurs and VCs can thus both take advantage by realising that this effect can play a role in VC decision making.
Several Entrepreneurs and VC themselves have also been asked3 if they thought the concreteness of the data presented in business plans plays a role in the realization of VCs’ overconfidence in their decision-making regarding it. They all thought that data concreteness indeed could feed this overconfidence.
3 The following VCs have been spoken: Jos Peeters founder and managing partner of Capricorn Venture
Partners (Belgium), Huseyin R Demirhisar founder and managing partner of Angel Wing Ventures, Ilan Goudsmit business analyst of Van den Ende & Deitmers. And the following entrepreneurs have been spoken: John Lee CEO and Co-founder of Folium Partners, Keith Lovell CFO of Shazam, Richard Guerin Vice President of Brightree.
Taken together, there are enough reasons to state that a new analysis of the relationship between data concreteness in business plan and overconfidence of VCs may be of value. Researchers with aspirations, attempting this, would be well advised to take notion of the limitations of this study, these will be discussed below.
5.2 Limitations
The experiment could not be conducted because there was not enough variance observed in the business plans on the concreteness scale, in order to divide them into two experimental groups. There are a two possible explanations for this.
Firstly, in a real life situation, other than an experimental setting, when VCs are sourcing for deals, the business plans mostly come in via their network. In this research set up, business plans were unsolicited presented. In reality according to a study of Diebold Group (1974) only 36% percent comes in this way. In the other 64% of the cases they come in via other VC companies, investment brokers, accountants and contact of partners. In these instances it is very likely that a VC already received some additional information about the entrepreneurs behind the venture in question. This knowledge makes it easier to imagine how a person with a particular background would be able to successfully build a company being able selling a new product or service in a new market. The lack of this knowledge in the pre-survey could have led to a situation in which VCs experience those plans as less concrete.
This effect could also have been compounded by the fact that the company names and brand names have been anonymized, VCs were also informed about this. This knowledge makes the evaluations for the VCs more artificial. But this could not have been avoided because otherwise VC could have been biased by personal
knowledge of the entrepreneur and/or the product in question.
The above may explain why the maximum upward deviation of all business plans on the seven point concreteness scale from the neutral point, 4, was only 0.22 whereas the maximum downward deviation was as much as 1.19.
5.3 Advice for VCs and entrepreneurs and Future Studies
How should VCs cope with the potential existence of the concrete/abstract effect in VC decision making? A research from Anderson (1983) which also used an experiment as research method compared the amount of belief perseverance, when beliefs are initially based on either abstract or concrete data. The amount of belief perseverance was measured after a thoroughly debriefing about the fictitious nature of their initial data. Anderson (1983) found significantly more perseverance in the concrete data. When they did an follow up study they found that significantly (X2 (1) = 5.19 p<.03) more
individuals engaged in spontaneous causal reasoning when confronted with concrete data, furthermore individuals who engaged in causal processing showed significantly more perseverance than those who listed no causal thoughts, F(1, 33) = 6.18, p<.02). VCs should learn from this that people in general believe that, like Anderson (1983) has formulated, “A” leads to “B” to the extent that they can easily imagine how or why “A leads to “B”. Or can construct a scenario in which “A” leads to “B”. Or when translated to venture investing how or why leads a particular marketing strategy,
business structure, sales strategy et cetera to a particular market share, sales turnover, gross margin et cetera, which will drive the venture’s value within a given timeframe.
Anderson (1983) suggests that constructing such scenario’s is more likely to occur with concrete data because that contains rich detail that may make explanations easier to generate. However this can be risky for VCs because the data which
entrepreneurs present about their venture or even VCs themselves gather about a the ventures would likely consist mainly of concrete data which are much weaker and would very much be dependent upon eventualities.
Since the evaluation process and the resulting selection decisions are crucial to the success of a VC firm, VCs are well advised to avoid any form of bias in their decision making. Earlier work from Anderson (1981) gives more insight in how the
perseverance bias, which can cause overconfidence in a VCs own judgement, can be reduced or ideally eliminated.
This research from Anderson (1981) compared the amount of belief
perseverance when beliefs are initially based on concrete data suggestive of either a positive or negative relationship between two variables, between a group of subjects which was induced to consider both relationships and a group who only had to consider the relationship suggested by the presented data. It turned out that subject forced to consider both relationships showed significantly less theory perseverance after a debriefing about the fictitious nature of the data. Although asking to think of counter explanations reduced the perseverance it did not entirely eliminate unwarranted theory perseverance.
These findings suggest that VCs can at least reduce the potential overconfidence bias, which is presumed to be caused by the theory perseverance, by forcing themselves to think of alternative or competing theory why “A” does not lead to “B”. Which is desirable, because then it is more likely that a VC can foresee potential pitfalls and therefore can carry out needed changes if the VC company is really going to invest.
Given the very low percentage of deal proposals which result in investments by VC firms, ventures seeking funding are well advised to make use of the potential concrete/abstract effect in venture capital. By presenting the data in such a way that VC companies can indeed easily construct a scenario themselves how their specific
approach probably leads in the end to a successful exit for a VC. Then is it likely that VCs hold on to the believes they initially developed themselves. With regard to this the CEO and co-founder (Brian Balduf from VHT, inc.) of a venture that succeeded in getting VC funding, phrased the challenge to get funded as follows: “The simpler and
more concrete the information is, the easier it is to get a deal done. The challenge is to boil down all the relevant info into an equation as simple as A+B = C and 2A+B = 4C, etc. Basically, have you proved the concept and does it scale.”
Unfortunately this research did not succeed in testing its hypothesis, so that supportive evidence could have been presented and directions for further research could have been presented. A new attempt to find support for this hypothesis could contribute
to our understanding of the realization of venture capital decision making. Once evidence have been founded, a fruitful direction for further research would then be the question how the effect of the discussed bias could be systematically reduced in an VC organization.
Although this study could not prove if the concrete/abstract effect plays a role in the realization of VC decision making, it should hopefully contribute to new studies attempting to prove it. Which in turn will contribute to the advancing of our
Appendix
Items to be answered by the ventures
Organisational performance
1. How many full-time workers are employed by your organisation for the year 2009? (i.e., who work 32 hours or more weekly) ……… persons
2. How many full-time-workers were employed by your organisation for the year 2012? (i.e., who work 32 hours or more weekly) ……… persons
3. How much is sales turnover for the year 2012? € ………..
4. How much is sales turnover for the year 2009? € ………..
To what extent are you satisfied with reaching the following organisational goal?
The first six items measure the venture’s subjective financial goals and the next six items measure the venture’s subjective non-financial goals. The twelve measures together capture the venture’s goal effectiveness. The measures are scaled 1 (very unsatisfied) to 5 (very satisfied).
unsatisfied satisfied
Sales growth
Market share
Gross margin
Return on investment (ROI)
Market value company shares
Liquidity position
Development of new products and
organisational processes
Development of target markets
Operational efficiency
employees' development
Firm's stability
Scores on the performance measures by venture:
The first involves organisational growth, this variable was computed by taking the mean of standardized scores of the four growth measures (average sales increase, average sales growth rate, average number of employees increase and average sales growth rate). The second is goal effectiveness, covered by the overall subjective
performance index. The third one is Labour productivity, gauged by the firm’s sales per employee ratio.
Organisational
growth Goal Effectiveness
Labour productivity UbiquiSys 0,901441 3,58 0,067239 VHT -0,08405 3,42 0,148714 Surewaves 0,604383 3,58 0,162516 Flat World Knowledge 0,152869 3,58 0,247407 Voice Sage -0,31051 4,08 0,35603 JustBookIt 0,989171 2 0,374467 Call Recall -0,47498 3,25 0,441526 Applian Sys -1,1583 3 0,731322 Assay Depot 0,246455 3,33 1,152951 OV-watch -0,86648 3,08 1,553652 µ 0 3,29 0,523582 0,578864 0,390267 0,373436
Ventures were considered succesful if they scored on two of the three measures above the group average of the total pool of 10 ventures. This were Ubiquisys, Surewaves, Flat World Knowledge, and Assay Depot. The rest were considered as unsuccesful ventures. The numbers above average are printed in bold.
Items to be answered by the venture capitalists after having been exposed to each decision case.
1. How likely is this venture to succeed? very unlikely to succeed
very likely to succeed
1 2 3 4 5 6 7
2. How confident are you about your success assessment?
1 2 3 4 5 6 7
1= no more confident the venture will succeed or fail than in predicting a head or a tails in a coin flip 7= "The venture exhibits all of the characteristics of a resounding success/failure"
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